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The organization ¶ EverFree develops and scales transformative solutions to end human trafficking so that all people can thrive in freedom and dignity. EverFree provides comprehensive, effective care for survivors of exploitation, and unites partners and communities to measure impact, improve care, and stop exploitation. The challenge ¶ As of 2021, an estimated 50 million people worldwide were living in modern slavery, forced to work or marry against their will.
Built the Freedom Lifemap platform, a digital tool designed to support survivors of human trafficking on their journey toward reintegration and independence Approaches include: Software engineering, user experience design, data engineering, product strategy Case study
This Good Tech Fest didnt start for me with Data science is bullsh*t, right? like last year, but the 2025 edition was just as provocative. After the conference, Ive been working to crystalize some of the conversations into trends in the sector. The conference was off-the-record, so please forgive me for not citing people directly. I want to start with a reflection that GTF is a gathering of doers.
Organizations run AI competitions for a variety of reasons. They want to engage the expertise of a global community. They want to push the limits of available methods for their needs. They want to explore innovative approaches and surface new ideas. They want to benchmark the level of performance that can be achieved with their data. At the end of a competition, these organizations get a few things: Winning solutions, consisting of research code in a Github repository and often shared openly for
The Challenge ¶ Alzheimer's disease and Alzheimer's disease-related dementias (AD/ADRD) are a group of brain disorders characterized by progressive cognitive impairments that severely impact daily functioning. Early prediction of AD/ADRD is crucial for potential disease modification through emerging treatments , but current methods are not sensitive enough to reliably detect the disease in its early or presymptomatic stages.
Unlike the well-established ecosystem for image processing, working with audio data often feels like navigating uncharted territory especially when developing acoustic features for machine learning. Image data are widely used in machine learning to support everything from wildlife research to cancer detection. These applications are all enabled by a strong ecosystem of open-source Python packages for working with image data.
Background ¶ Howdy! This is a post with code that builds a benchmark for our What's Up, Docs? Document Summarization with LLMs practice competition. You're likely here because you've heard about Large Language Models (LLMs) and maybe you've used them some in the past, but you're interested in see how they can be used programmatically to solve problems that might be important to you.
While most speech models focus on adults, this challenge focused on evaluating the speech of young children at an age at which there is significant variation across individuals. By advancing automated scoring for literacy screeners, these innovations have the potential to transform classrooms and improve outcomes for young learners. Dr. Satrajit Ghosh, Director, Senseable Intelligence Group Literacythe ability to read, write, and comprehend languageis a fundamental skill that underlies personal
Last year, I set my new year's resolution to get a data science job dedicated to helping fix social and environmental problems. It was very difficult at first to find tech jobs for good, but as I kept searching online and doing research, I was able to find many exciting organizations who were using data science and machine learning to improve healthcare, monitor and prevent climate change, and improve government services.
Suicide is one of the leading causes of death in the United States for 5-24 year-olds. Researchers and policymakers study the circumstances of youth suicides to better understand them and reduce their occurrence. One key source of information is the National Violent Death Reporting System (NVDRS). The NVDRS captures information about violent deaths across the United States that has been abstracted from sources including law enforcement reports, coroner/medical examiner reports, toxicology report
Accurate seasonal water supply forecasts are crucial for effective water resources management in the Western United States. This region faces dry conditions and high demand for water, and these forecasts are essential for making informed decisions. They guide everything from water supply management and flood control to hydropower generation and environmental objectives.
Looking back ¶ When we started DrivenData in 2014, the application of data science for social good was in its infancy. There was rapidly growing demand for data science skills at companies like Netflix and Amazon. Prominent use cases focused on marketing and content recommendations. Applications of data science for nonprofits, NGOs, social enterprises and government services were nearly nonexistent.
Welcome! This guest post from our partners at the MIT Gabrieli Lab will guide you through building a simple baseline model for the Goodnight Moon, Hello Early Literacy Screening Challenge. This benchmark will predict scores for literacy screening tasks using extracted audio features. For access to the data used in this benchmark notebook, sign up for the competition here.
Processing mental health narratives with open-source LLMs ¶ Welcome to the benchmark notebook for the Automated Abstraction track of Youth Mental Health Narratives challenge. For access to the data used in this benchmark notebook, sign up for the competition here. Background ¶ The National Violent Death Reporting System (NVDRS) is a dataset maintained by the Center for Disease Control's Center for Injury Prevention and Control that contains narratives from law enforcement, coroner/medi
The Challenge ¶ Alzheimer's disease and Alzheimer's disease-related dementias (AD/ADRD) are a group of brain disorders characterized by progressive cognitive impairments that severely impact daily functioning. Early prediction of AD/ADRD is crucial for potential disease modification through emerging treatments , but current methods are not sensitive enough to reliably detect the disease in its early or presymptomatic stages.
Machine learning competitions offer rich opportunities for learning and teaching. Competitions provide an experiential learning environment, featuring a motivating problem, a clear objective, access to all necessary materials and tools, and iterative feedback. As a result, we often see competitions used by instructors to build and demonstrate applied data skills.
Accurate seasonal water supply forecasts are crucial for effective water resources management in the Western United States. This region faces dry conditions and high demand for water, and these forecasts are essential for making informed decisions. They guide everything from water supply management and flood control to hydropower generation and environmental objectives.
The original Cookiecutter Data Science (CCDS) was published over 8 years ago. The goal was, as the tagline states “a logical, reasonably standardized but flexible project structure for data science.” That version , now affectionately called V1, has been a workhorse for a long time, and got the job done for many projects while being mostly unchanged.
Data science has enormous potential to improve lives, from detecting cancer to responding to flood disaster events. However, these benefits are not felt equally. Implementing advanced machine learning methods requires training, resources, and time, making data science work subject to existing widespread inequalities based on race, gender, geography, and more.
The Challenge ¶ Motivation ¶ Much of the world's healthcare data is stored in free-text documents, usually clinical notes taken by doctors. This unstructured data can be challenging to analyze and extract meaningful insights from. However, by applying a standardized terminology like SNOMED CT, healthcare organizations can convert this free-text data into a structured format that can be readily analyzed by computers, in turn stimulating the development of new medicines, treatment pathwa
The Challenge ¶ “Underwater kelp forests nurture vibrant and diverse ecosystems around the world. Kelp forest health can be threatened by marine heat waves, shifts in grazers, poor water quality (often due to run off linked to changes in nearby landscapes), and over-harvesting. These pressures can manifest locally or globally, and with complicated spatial dynamics.
NASA's commitment to open data sharing empowers global efforts to tackle urgent issues, such as the Sustainable Development Goals. It’s exciting to see the innovative solutions being proposed by these diverse teams from around the world. Dr. Steve Crawford, Senior Program Executive for Scientific Data and Computing at NASA Background ¶ Our world is facing many urgent challenges, such as climate change, water insecurity, and food insecurity.
Welcome to the benchmark notebook for the Pose Bowl: Object Detection challenge! If you are just getting started, first checkout the competition homepage and problem description. Pose Bowl: Object Detection ¶ In this challenge, you will help to develop new methods for conducting spacecraft inspections by identifying the position and orientation of a target spacecraft in an image.
Welcome! This guest blog post from our partners at Veratai contains code for training the benchmark entity linking model for the SNOMED CT Entity Linking Challenge. You can find the code and instructions for reproducing this notebook in this repository. Background ¶ Much of the world's healthcare data is stored in free-text documents, usually clinical notes taken by doctors.
We are very interested in how AI-based research assistants can help NASA, and we received a diverse variety of cutting-edge AI approaches from around the globe in the Research Rovers challenge. We are inspired by the prototypes the teams made and look forward to using AI-based research assistants to multiply NASA’s work in safe, secure, responsive and powerful ways.
The challenge ¶ Falls are the leading cause of injury-related deaths among adults 65 and older. Fall risk can be mitigated through treatment of vision problems, exercise for strength and balance, removal of tripping hazards, and other interventions that appropriately target common fall causes. In the Unsupervised Wisdom Challenge , participants were tasked with identifying novel, effective methods of using unsupervised machine learning to extract insights about older adult falls from narrat
This is a follow-on post to a previous summary of the ways that organizations are using AI for climate action. It consolidates a range of real-world datasets from DrivenData challenges for anyone looking to get hands-on experience. Climate change poses a massive challenge, with far-reaching impacts. Many data scientists and developers are aware of the need for action and are interested in finding problems to take on.
Our world is facing many urgent challenges, like climate change, water insecurity, and food insecurity. One critical tool for addressing these challenges is Earth observation data , meaning data that is gathered in outer space about life here on Earth! Earth observation data provide accurate information on our atmosphere, oceans, ecosystems, land cover, and built environment.
Hey! I'm Chris, and I'm a data scientist at DrivenData. I wanted to write this blog post because, while we work with satellite data frequently at DrivenData, I personally had not had that experience yet. Since I’m a beginner, I thought I would document the process of learning about satellite data to help others who are also trying to understand more about this data on their learning journeys.
Our team is excited to share a new feature that you will see in DrivenData competitions: Community Code! The Community Code section is a place where participants can share helpful code related to the competition. This could be anything from short snippets demonstrating some data processing to longer tutorials or analyses. Community Code will be enabled on a competition-by-competition basis.
How can we use data and AI to help combat the effects of climate change? ¶ The current generation of humanity is faced with a massive challenge. The climate around us is changing, and how we mitigate and adapt to its effects will have deep and lasting impacts for ourselves, our children, other species, and the planet. A challenge of this magnitude calls for the best ideas, tools and technologies we have to offer.
The challenge ¶ The objective of the VisioMel Challenge was to explore the feasibility of an algorithm that can predict the recurrence of melanomas diagnosed at a localized stage. The results already show that the algorithms are at least on par with the traditional prognostic factors. In the near future, it will undoubtedly be possible to have a predictive digital signature of melanoma recurrence.
Video Similarity Challenge ¶ Meet the top teams who identified copied and manipulated videos! ¶ Participants in the Meta AI Video Similarity Challenge found creative ways to improve representations used for copy detection, as well as localization techniques that allow copied sections to be identified efficiently within longer videos. Ed Pizzi, Meta AI Research Scientist and Video Similarity Challenge author The ability to identify and track content on social media platforms, called con
The Challenge ¶ Motivation ¶ Coordinating our nation's airways is the role of the National Airspace System (NAS). The NAS is arguably the most complex transportation system in the world. Operational changes can save or cost airlines, taxpayers, consumers, and the economy at large thousands to millions of dollars on a regular basis. The NAS is investing in new ways to bring vast amounts of data together with state-of-the-art machine learning to improve air travel for everyone.
The Community Spotlight celebrates the diversity of expertise, perspectives, and experiences of our community members. In this post we sit down with Dr. Helen Yannakoudakis, a winner of the Hateful Memes competition and an Assistant Professor at King’s College London, Visiting Researcher at the University of Cambridge, and co-founder and Chief Scientific Officer at Kinhub.
The Community Spotlight celebrates the diversity of expertise, perspectives, and experiences of our community members. In this post we sit down with Brett Mullins, a winner of the Differential Privacy Temporal Map Challenge and a graduate student at the University of Massachusetts at Amherst. Name: Brett Mullins ¶ Hometown: Atlanta, Georgia ¶ Tell us a little about yourself.
The Challenge ¶ There are tens of thousands of lakes that matter for recreation and drinking water. Cyanobacterial blooms pose real risks in many of them, and we really don't know when or where they show up, except in the largest lakes. Satellites can help, but the high resolution satellites were not designed for this purpose, and simple methods to find these blooms will often mark features that aren't blooms.
Meet April ¶ On April 1st, DrivenData released a splashily-titled new competition: "Preview: The Future of DrivenData Competitions". The competition announced the debut of DrivenData's own chatbot, April, who was developed to take over evaluation of all competitions. If you tried submitting prediction to April, you likely found that she was very hard to impress.
Privacy-enhancing technologies (PETs) have the potential to unlock more trustworthy innovation in data analysis and machine learning. Federated learning is one such technology that enables organizations to analyze sensitive data while providing improved privacy protections. These technologies could advance innovation and collaboration in new fields and help harness the power of data to tackle some of our most pressing societal challenges.
The Challenge ¶ “I believe that we are just at the beginning of the Earth Observation big data revolution. Joint effort and open science are the fundamental bricks to build new models and services for addressing societal challenges.” Dr. Andrea Nascetti, Faculty of Sciences, University of Liège Motivation ¶ Forests are adding and removing carbon dioxide from the air all the time.
VisioMel Challenge: Predicting Melanoma Relapse ¶ Welcome to the VisioMel Challenge: Predicting Melanoma Relapse ! In this benchmark blog post, we will show you how to load the data for the challenge, make a prediction, and package your materials for a submission. Along the way, we'll include some tips that might be useful as you improve on this benchmark model and help solve a critical problem in pathology and melanoma treatment.
You may have heard there are new, modern standards in Python packaging ( pyproject.toml !) that have been adopted over the last few years. There are now several popular and shiny modern tools for managing your packaging projects. (Poetry! Hatch! PDM!) However, the documentation is scattered and much of it is specific to these competing tools. What are the recommended best practices when creating a Python package?
Pushback to the Future: Predict Pushback Time at US Airports - Benchmark ¶ Coordinating our nation’s airways is the role of the National Airspace System (NAS). The NAS is one of the most complex transportation systems in the world. Operational changes can save or cost airlines, taxpayers, consumers, and the economy at large thousands to millions of dollars on a regular basis.
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